chengyongruandXubin Ren
da225fc24e
Use OpenAI's SDK default base URL for Quick Start model discovery without writing that default into the saved provider config.
1617 lines
59 KiB
Python
1617 lines
59 KiB
Python
"""Unit tests for onboard core logic functions.
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These tests focus on the business logic behind the onboard wizard,
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without testing the interactive UI components.
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"""
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Any, cast
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from pydantic import BaseModel, Field
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from nanobot.cli import onboard as onboard_wizard
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from nanobot.cli.commands import _merge_missing_defaults
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from nanobot.cli.onboard import (
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_BACK_PRESSED,
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_configure_pydantic_model,
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_format_value,
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_get_constraint_hint,
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_get_field_display_name,
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_get_field_type_info,
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_input_text,
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run_onboard,
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)
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from nanobot.config.schema import Config, ModelPresetConfig
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from nanobot.utils.helpers import sync_workspace_templates
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class TestMergeMissingDefaults:
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"""Tests for _merge_missing_defaults recursive config merging."""
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def test_adds_missing_top_level_keys(self):
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existing = {"a": 1}
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defaults = {"a": 1, "b": 2, "c": 3}
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result = _merge_missing_defaults(existing, defaults)
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assert result == {"a": 1, "b": 2, "c": 3}
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def test_preserves_existing_values(self):
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existing = {"a": "custom_value"}
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defaults = {"a": "default_value"}
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result = _merge_missing_defaults(existing, defaults)
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assert result == {"a": "custom_value"}
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def test_merges_nested_dicts_recursively(self):
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existing = {
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"level1": {
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"level2": {
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"existing": "kept",
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}
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}
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}
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defaults = {
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"level1": {
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"level2": {
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"existing": "replaced",
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"added": "new",
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},
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"level2b": "also_new",
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}
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}
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result = _merge_missing_defaults(existing, defaults)
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assert result == {
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"level1": {
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"level2": {
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"existing": "kept",
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"added": "new",
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},
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"level2b": "also_new",
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}
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}
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def test_returns_existing_if_not_dict(self):
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assert _merge_missing_defaults("string", {"a": 1}) == "string"
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assert _merge_missing_defaults([1, 2, 3], {"a": 1}) == [1, 2, 3]
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assert _merge_missing_defaults(None, {"a": 1}) is None
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assert _merge_missing_defaults(42, {"a": 1}) == 42
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def test_returns_existing_if_defaults_not_dict(self):
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assert _merge_missing_defaults({"a": 1}, "string") == {"a": 1}
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assert _merge_missing_defaults({"a": 1}, None) == {"a": 1}
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def test_handles_empty_dicts(self):
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assert _merge_missing_defaults({}, {"a": 1}) == {"a": 1}
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assert _merge_missing_defaults({"a": 1}, {}) == {"a": 1}
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assert _merge_missing_defaults({}, {}) == {}
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def test_backfills_channel_config(self):
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"""Real-world scenario: backfill missing channel fields."""
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existing_channel = {
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"enabled": False,
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"appId": "",
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"secret": "",
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}
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default_channel = {
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"enabled": False,
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"appId": "",
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"secret": "",
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"msgFormat": "plain",
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"allowFrom": [],
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}
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result = _merge_missing_defaults(existing_channel, default_channel)
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assert result["msgFormat"] == "plain"
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assert result["allowFrom"] == []
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class TestGetFieldTypeInfo:
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"""Tests for _get_field_type_info type extraction."""
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def test_extracts_str_type(self):
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class Model(BaseModel):
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field: str
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type_name, inner = _get_field_type_info(Model.model_fields["field"])
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assert type_name == "str"
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assert inner is None
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def test_extracts_int_type(self):
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class Model(BaseModel):
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count: int
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type_name, inner = _get_field_type_info(Model.model_fields["count"])
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assert type_name == "int"
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assert inner is None
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def test_extracts_bool_type(self):
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class Model(BaseModel):
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enabled: bool
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type_name, inner = _get_field_type_info(Model.model_fields["enabled"])
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assert type_name == "bool"
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assert inner is None
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def test_extracts_float_type(self):
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class Model(BaseModel):
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ratio: float
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type_name, inner = _get_field_type_info(Model.model_fields["ratio"])
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assert type_name == "float"
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assert inner is None
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def test_extracts_list_type_with_item_type(self):
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class Model(BaseModel):
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items: list[str]
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type_name, inner = _get_field_type_info(Model.model_fields["items"])
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assert type_name == "list"
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assert inner is str
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def test_extracts_list_type_without_item_type(self):
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# Plain list without type param falls back to str
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class Model(BaseModel):
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items: list # type: ignore
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# Plain list annotation doesn't match list check, returns str
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type_name, inner = _get_field_type_info(Model.model_fields["items"])
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assert type_name == "str" # Falls back to str for untyped list
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assert inner is None
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def test_extracts_dict_type(self):
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# Plain dict without type param falls back to str
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class Model(BaseModel):
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data: dict # type: ignore
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# Plain dict annotation doesn't match dict check, returns str
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type_name, inner = _get_field_type_info(Model.model_fields["data"])
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assert type_name == "str" # Falls back to str for untyped dict
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assert inner is None
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def test_extracts_optional_type(self):
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class Model(BaseModel):
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optional: str | None = None
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type_name, inner = _get_field_type_info(Model.model_fields["optional"])
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# Should unwrap Optional and get str
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assert type_name == "str"
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assert inner is None
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def test_extracts_nested_model_type(self):
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class Inner(BaseModel):
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x: int
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class Outer(BaseModel):
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nested: Inner
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type_name, inner = _get_field_type_info(Outer.model_fields["nested"])
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assert type_name == "model"
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assert inner is Inner
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def test_handles_none_annotation(self):
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"""Field with None annotation defaults to str."""
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class Model(BaseModel):
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field: Any = None
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# Create a mock field_info with None annotation
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field_info = SimpleNamespace(annotation=None)
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type_name, inner = _get_field_type_info(field_info)
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assert type_name == "str"
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assert inner is None
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def test_literal_type_returns_literal_with_choices(self):
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"""Literal["a", "b"] should return ("literal", ["a", "b"])."""
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from typing import Literal
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class Model(BaseModel):
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mode: Literal["standard", "persistent"] = "standard"
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type_name, inner = _get_field_type_info(Model.model_fields["mode"])
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assert type_name == "literal"
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assert inner == ["standard", "persistent"]
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def test_real_provider_retry_mode_field(self):
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"""Validate against actual AgentDefaults.provider_retry_mode field."""
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from nanobot.config.schema import AgentDefaults
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type_name, inner = _get_field_type_info(AgentDefaults.model_fields["provider_retry_mode"])
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assert type_name == "literal"
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assert inner == ["standard", "persistent"]
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class TestGetFieldDisplayName:
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"""Tests for _get_field_display_name human-readable name generation."""
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def test_uses_description_if_present(self):
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class Model(BaseModel):
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api_key: str = Field(description="API Key for authentication")
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name = _get_field_display_name("api_key", Model.model_fields["api_key"])
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assert name == "API Key for authentication"
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def test_converts_snake_case_to_title(self):
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field_info = SimpleNamespace(description=None)
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name = _get_field_display_name("user_name", field_info)
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assert name == "User Name"
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def test_adds_url_suffix(self):
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field_info = SimpleNamespace(description=None)
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name = _get_field_display_name("api_url", field_info)
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# Title case: "Api Url"
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assert "Url" in name and "Api" in name
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def test_adds_path_suffix(self):
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field_info = SimpleNamespace(description=None)
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name = _get_field_display_name("file_path", field_info)
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assert "Path" in name and "File" in name
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def test_adds_id_suffix(self):
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field_info = SimpleNamespace(description=None)
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name = _get_field_display_name("user_id", field_info)
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# Title case: "User Id"
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assert "Id" in name and "User" in name
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def test_adds_key_suffix(self):
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field_info = SimpleNamespace(description=None)
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name = _get_field_display_name("api_key", field_info)
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assert "Key" in name and "Api" in name
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def test_adds_token_suffix(self):
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field_info = SimpleNamespace(description=None)
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name = _get_field_display_name("auth_token", field_info)
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assert "Token" in name and "Auth" in name
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def test_adds_seconds_suffix(self):
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field_info = SimpleNamespace(description=None)
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name = _get_field_display_name("timeout_s", field_info)
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# Contains "(Seconds)" with title case
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assert "(Seconds)" in name or "(seconds)" in name
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def test_adds_ms_suffix(self):
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field_info = SimpleNamespace(description=None)
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name = _get_field_display_name("delay_ms", field_info)
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# Contains "(Ms)" or "(ms)"
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assert "(Ms)" in name or "(ms)" in name
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class TestFormatValue:
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"""Tests for _format_value display formatting."""
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def test_formats_none_as_not_set(self):
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assert "not set" in _format_value(None)
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def test_formats_empty_string_as_not_set(self):
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assert "not set" in _format_value("")
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def test_formats_empty_dict_as_not_set(self):
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assert "not set" in _format_value({})
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def test_formats_empty_list_as_not_set(self):
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assert "not set" in _format_value([])
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def test_formats_string_value(self):
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result = _format_value("hello")
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assert "hello" in result
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def test_formats_list_value(self):
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result = _format_value(["a", "b"])
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assert "a" in result or "b" in result
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def test_formats_dict_value(self):
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result = _format_value({"key": "value"})
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assert "key" in result or "value" in result
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def test_formats_int_value(self):
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result = _format_value(42)
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assert "42" in result
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def test_formats_bool_true(self):
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result = _format_value(True)
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assert "true" in result.lower() or "✓" in result
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def test_formats_bool_false(self):
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result = _format_value(False)
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assert "false" in result.lower() or "✗" in result
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class TestSyncWorkspaceTemplates:
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"""Tests for sync_workspace_templates file synchronization."""
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def test_creates_missing_files(self, tmp_path):
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"""Should create template files that don't exist."""
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workspace = tmp_path / "workspace"
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added = sync_workspace_templates(workspace, silent=True)
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# Check that some files were created
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assert isinstance(added, list)
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# The actual files depend on the templates directory
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def test_does_not_overwrite_existing_files(self, tmp_path):
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"""Should not overwrite files that already exist."""
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workspace = tmp_path / "workspace"
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workspace.mkdir(parents=True)
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(workspace / "AGENTS.md").write_text("existing content")
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sync_workspace_templates(workspace, silent=True)
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# Existing file should not be changed
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content = (workspace / "AGENTS.md").read_text()
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assert content == "existing content"
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def test_does_not_create_tools_md(self, tmp_path):
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"""Tool contract is injected internally, not copied into user workspaces."""
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workspace = tmp_path / "workspace"
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added = sync_workspace_templates(workspace, silent=True)
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assert "TOOLS.md" not in added
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assert not (workspace / "TOOLS.md").exists()
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def test_preserves_existing_tools_md_without_overwriting(self, tmp_path):
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"""Legacy user workspaces may have TOOLS.md; sync should leave it untouched."""
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workspace = tmp_path / "workspace"
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workspace.mkdir(parents=True)
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tools_path = workspace / "TOOLS.md"
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tools_path.write_text("custom tool notes", encoding="utf-8")
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sync_workspace_templates(workspace, silent=True)
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assert tools_path.read_text(encoding="utf-8") == "custom tool notes"
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def test_creates_memory_directory(self, tmp_path):
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"""Should create memory directory structure."""
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workspace = tmp_path / "workspace"
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sync_workspace_templates(workspace, silent=True)
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assert (workspace / "memory").exists() or (workspace / "skills").exists()
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def test_returns_list_of_added_files(self, tmp_path):
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"""Should return list of relative paths for added files."""
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workspace = tmp_path / "workspace"
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added = sync_workspace_templates(workspace, silent=True)
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assert isinstance(added, list)
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# All paths should be relative to workspace
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for path in added:
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assert not Path(path).is_absolute()
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class TestProviderChannelInfo:
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"""Tests for provider and channel info retrieval."""
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def test_get_provider_names_returns_dict(self):
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from nanobot.cli.onboard import _get_provider_names
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names = _get_provider_names()
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assert isinstance(names, dict)
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assert len(names) > 0
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# Should include common providers
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assert "openai" in names or "anthropic" in names
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assert "openai_codex" not in names
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assert "github_copilot" not in names
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def test_get_channel_names_returns_dict(self):
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from nanobot.cli.onboard import _get_channel_names
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names = _get_channel_names()
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assert isinstance(names, dict)
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# Should include at least some channels
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assert len(names) >= 0
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def test_get_provider_info_returns_valid_structure(self):
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from nanobot.cli.onboard import _get_provider_info
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info = _get_provider_info()
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assert isinstance(info, dict)
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# Each value should be a tuple with expected structure
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for provider_name, value in info.items():
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assert isinstance(value, tuple)
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assert len(value) == 4 # (display_name, needs_api_key, needs_api_base, env_var)
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class _SimpleDraftModel(BaseModel):
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api_key: str = ""
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class _NestedDraftModel(BaseModel):
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api_key: str = ""
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class _OuterDraftModel(BaseModel):
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nested: _NestedDraftModel = Field(default_factory=_NestedDraftModel)
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class TestConfigurePydanticModelDrafts:
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@staticmethod
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def _patch_prompt_helpers(monkeypatch, tokens, text_value="secret"):
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sequence = iter(tokens)
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def fake_select(_prompt, choices, default=None):
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token = next(sequence)
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if token == "first":
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return choices[0]
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if token == "done":
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return "[Done]"
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if token == "back":
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return _BACK_PRESSED
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return token
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monkeypatch.setattr(onboard_wizard, "_select_with_back", fake_select)
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monkeypatch.setattr(onboard_wizard, "_show_config_panel", lambda *_args, **_kwargs: None)
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monkeypatch.setattr(
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onboard_wizard, "_input_with_existing", lambda *_args, **_kwargs: text_value
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)
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def test_back_commits_section_draft(self, monkeypatch):
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model = _SimpleDraftModel()
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self._patch_prompt_helpers(monkeypatch, ["first", "back"])
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result = _configure_pydantic_model(model, "Simple")
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assert result is not None
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updated = cast(_SimpleDraftModel, result)
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assert updated.api_key == "secret"
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assert model.api_key == ""
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def test_cancel_keeps_original_model_unchanged(self, monkeypatch):
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model = _SimpleDraftModel()
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self._patch_prompt_helpers(monkeypatch, ["first", None])
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result = _configure_pydantic_model(model, "Simple")
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assert result is None
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assert model.api_key == ""
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def test_completing_section_returns_updated_draft(self, monkeypatch):
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model = _SimpleDraftModel()
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self._patch_prompt_helpers(monkeypatch, ["first", "done"])
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result = _configure_pydantic_model(model, "Simple")
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assert result is not None
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updated = cast(_SimpleDraftModel, result)
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assert updated.api_key == "secret"
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assert model.api_key == ""
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def test_nested_section_back_commits_nested_edits(self, monkeypatch):
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model = _OuterDraftModel()
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self._patch_prompt_helpers(monkeypatch, ["first", "first", "back", "done"])
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result = _configure_pydantic_model(model, "Outer")
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assert result is not None
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updated = cast(_OuterDraftModel, result)
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assert updated.nested.api_key == "secret"
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assert model.nested.api_key == ""
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def test_nested_section_done_commits_nested_edits(self, monkeypatch):
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model = _OuterDraftModel()
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self._patch_prompt_helpers(monkeypatch, ["first", "first", "done", "done"])
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result = _configure_pydantic_model(model, "Outer")
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assert result is not None
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updated = cast(_OuterDraftModel, result)
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assert updated.nested.api_key == "secret"
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assert model.nested.api_key == ""
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class TestRunOnboardExitBehavior:
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def test_main_menu_interrupt_can_discard_unsaved_session_changes(self, monkeypatch):
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initial_config = Config()
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responses = iter(
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[
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"[A] Advanced Settings",
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"[A] Agent Settings",
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KeyboardInterrupt(),
|
|
"[X] Exit Without Saving",
|
|
]
|
|
)
|
|
|
|
class FakePrompt:
|
|
def __init__(self, response):
|
|
self.response = response
|
|
|
|
def ask(self):
|
|
if isinstance(self.response, BaseException):
|
|
raise self.response
|
|
return self.response
|
|
|
|
def fake_select(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
def fake_configure_general_settings(config, section):
|
|
if section == "Agent Settings":
|
|
config.agents.defaults.model = "test/provider-model"
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_main_menu_header", lambda: None)
|
|
monkeypatch.setattr(onboard_wizard, "questionary", SimpleNamespace(select=fake_select))
|
|
monkeypatch.setattr(onboard_wizard, "_configure_general_settings", fake_configure_general_settings)
|
|
|
|
result = run_onboard(initial_config=initial_config)
|
|
|
|
assert result.should_save is False
|
|
assert result.config.model_dump(by_alias=True) == initial_config.model_dump(by_alias=True)
|
|
|
|
|
|
class TestValidateFieldConstraint:
|
|
"""Tests for _validate_field_constraint schema-aware input validation."""
|
|
|
|
def test_returns_none_when_no_constraints(self):
|
|
"""Fields without constraints should pass validation."""
|
|
from pydantic import BaseModel
|
|
|
|
class M(BaseModel):
|
|
name: str = "hello"
|
|
|
|
field_info = M.model_fields["name"]
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
|
|
assert _validate_field_constraint("anything", field_info) is None
|
|
|
|
def test_rejects_value_below_ge_bound(self):
|
|
"""Value below ge (>=) bound should return error."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
count: int = Field(default=3, ge=0)
|
|
|
|
field_info = M.model_fields["count"]
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
|
|
result = _validate_field_constraint(-1, field_info)
|
|
assert result is not None
|
|
assert "0" in result
|
|
|
|
def test_accepts_value_at_ge_bound(self):
|
|
"""Value exactly at ge (>=) bound should pass."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
count: int = Field(default=3, ge=0)
|
|
|
|
field_info = M.model_fields["count"]
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
|
|
assert _validate_field_constraint(0, field_info) is None
|
|
|
|
def test_rejects_value_above_le_bound(self):
|
|
"""Value above le (<=) bound should return error."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
retries: int = Field(default=3, le=10)
|
|
|
|
field_info = M.model_fields["retries"]
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
|
|
result = _validate_field_constraint(11, field_info)
|
|
assert result is not None
|
|
assert "10" in result
|
|
|
|
def test_accepts_value_at_le_bound(self):
|
|
"""Value exactly at le (<=) bound should pass."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
retries: int = Field(default=3, le=10)
|
|
|
|
field_info = M.model_fields["retries"]
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
|
|
assert _validate_field_constraint(10, field_info) is None
|
|
|
|
def test_combined_ge_and_le_bounds(self):
|
|
"""Field with both ge and le should validate both."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
retries: int = Field(default=3, ge=0, le=10)
|
|
|
|
field_info = M.model_fields["retries"]
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
|
|
assert _validate_field_constraint(5, field_info) is None
|
|
assert _validate_field_constraint(-1, field_info) is not None
|
|
assert _validate_field_constraint(11, field_info) is not None
|
|
|
|
def test_gt_and_lt_bounds(self):
|
|
"""Strict inequality bounds (gt, lt) should exclude boundary."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
ratio: float = Field(default=0.5, gt=0.0, lt=1.0)
|
|
|
|
field_info = M.model_fields["ratio"]
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
|
|
assert _validate_field_constraint(0.5, field_info) is None
|
|
assert _validate_field_constraint(0.0, field_info) is not None
|
|
assert _validate_field_constraint(1.0, field_info) is not None
|
|
|
|
def test_min_length_constraint(self):
|
|
"""min_length should validate string/list length."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
name: str = Field(default="x", min_length=1)
|
|
|
|
field_info = M.model_fields["name"]
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
|
|
assert _validate_field_constraint("a", field_info) is None
|
|
assert _validate_field_constraint("", field_info) is not None
|
|
|
|
def test_max_length_constraint(self):
|
|
"""max_length should validate string/list length."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
tag: str = Field(default="x", max_length=5)
|
|
|
|
field_info = M.model_fields["tag"]
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
|
|
assert _validate_field_constraint("abc", field_info) is None
|
|
assert _validate_field_constraint("abcdef", field_info) is not None
|
|
|
|
def test_real_send_max_retries_field(self):
|
|
"""Validate against the actual ChannelsConfig.send_max_retries field."""
|
|
from nanobot.cli.onboard import _validate_field_constraint
|
|
from nanobot.config.schema import ChannelsConfig
|
|
|
|
field_info = ChannelsConfig.model_fields["send_max_retries"]
|
|
assert _validate_field_constraint(3, field_info) is None
|
|
assert _validate_field_constraint(0, field_info) is None
|
|
assert _validate_field_constraint(10, field_info) is None
|
|
assert _validate_field_constraint(-1, field_info) is not None
|
|
assert _validate_field_constraint(11, field_info) is not None
|
|
|
|
|
|
class TestGetConstraintHint:
|
|
"""Tests for _get_constraint_hint field display suffix."""
|
|
|
|
def test_no_constraints_returns_empty(self):
|
|
"""Fields without constraints should return empty string."""
|
|
from pydantic import BaseModel
|
|
|
|
class M(BaseModel):
|
|
name: str = "hello"
|
|
|
|
field_info = M.model_fields["name"]
|
|
assert _get_constraint_hint(field_info) == ""
|
|
|
|
def test_ge_le_range(self):
|
|
"""Field with ge+le should show '(min-max)'."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
retries: int = Field(default=3, ge=0, le=10)
|
|
|
|
field_info = M.model_fields["retries"]
|
|
hint = _get_constraint_hint(field_info)
|
|
assert "0" in hint
|
|
assert "10" in hint
|
|
|
|
def test_ge_only(self):
|
|
"""Field with only ge should show '(>= N)'."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
count: int = Field(default=1, ge=0)
|
|
|
|
field_info = M.model_fields["count"]
|
|
hint = _get_constraint_hint(field_info)
|
|
assert "0" in hint
|
|
assert ">=" in hint
|
|
|
|
def test_le_only(self):
|
|
"""Field with only le should show '(<= N)'."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
ratio: float = Field(default=1.0, le=100.0)
|
|
|
|
field_info = M.model_fields["ratio"]
|
|
hint = _get_constraint_hint(field_info)
|
|
assert "100" in hint
|
|
assert "<=" in hint
|
|
|
|
def test_real_send_max_retries_hint(self):
|
|
"""Actual ChannelsConfig.send_max_retries should show '(0-10)'."""
|
|
from nanobot.config.schema import ChannelsConfig
|
|
|
|
field_info = ChannelsConfig.model_fields["send_max_retries"]
|
|
hint = _get_constraint_hint(field_info)
|
|
assert "0" in hint
|
|
assert "10" in hint
|
|
|
|
|
|
class TestInputTextWithValidation:
|
|
"""Tests for _input_text integration with constraint validation."""
|
|
|
|
def test_rejects_out_of_range_int(self, monkeypatch):
|
|
"""_input_text with field_info should reject values violating ge/le constraints."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
retries: int = Field(default=3, ge=0, le=10)
|
|
|
|
field_info = M.model_fields["retries"]
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_get_questionary",
|
|
lambda: SimpleNamespace(text=lambda *a, **kw: SimpleNamespace(ask=lambda: "15")),
|
|
)
|
|
|
|
result = _input_text("Retries", 3, "int", field_info=field_info)
|
|
assert result is None
|
|
|
|
def test_accepts_valid_int(self, monkeypatch):
|
|
"""_input_text with field_info should accept valid constrained values."""
|
|
from pydantic import BaseModel, Field
|
|
|
|
class M(BaseModel):
|
|
retries: int = Field(default=3, ge=0, le=10)
|
|
|
|
field_info = M.model_fields["retries"]
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_get_questionary",
|
|
lambda: SimpleNamespace(text=lambda *a, **kw: SimpleNamespace(ask=lambda: "5")),
|
|
)
|
|
|
|
result = _input_text("Retries", 3, "int", field_info=field_info)
|
|
assert result == 5
|
|
|
|
def test_works_without_field_info(self, monkeypatch):
|
|
"""_input_text without field_info should work as before (no validation)."""
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_get_questionary",
|
|
lambda: SimpleNamespace(text=lambda *a, **kw: SimpleNamespace(ask=lambda: "42")),
|
|
)
|
|
|
|
result = _input_text("Count", 0, "int")
|
|
assert result == 42
|
|
|
|
|
|
class TestChannelCommonRegistration:
|
|
"""Tests for Channel Common menu registration."""
|
|
|
|
def test_channel_common_in_settings_sections(self):
|
|
"""Channel Common should be registered in _SETTINGS_SECTIONS."""
|
|
from nanobot.cli.onboard import _SETTINGS_SECTIONS
|
|
|
|
assert "Channel Common" in _SETTINGS_SECTIONS
|
|
|
|
def test_channel_common_getter_returns_channels(self):
|
|
"""Channel Common getter should return config.channels."""
|
|
from nanobot.cli.onboard import _SETTINGS_GETTER
|
|
|
|
config = Config()
|
|
result = _SETTINGS_GETTER["Channel Common"](config)
|
|
assert result is config.channels
|
|
|
|
def test_channel_common_setter_writes_channels(self):
|
|
"""Channel Common setter should update config.channels."""
|
|
from nanobot.cli.onboard import _SETTINGS_SETTER
|
|
|
|
config = Config()
|
|
original = config.channels
|
|
new_channels = original.model_copy(deep=True)
|
|
new_channels.send_tool_hints = True
|
|
_SETTINGS_SETTER["Channel Common"](config, new_channels)
|
|
assert config.channels.send_tool_hints is True
|
|
|
|
def test_channel_common_edit_preserves_extras(self):
|
|
"""Editing Channel Common should not lose per-channel extras."""
|
|
config = Config()
|
|
config.channels.feishu = {"enabled": True, "appId": "test123"}
|
|
channels = config.channels.model_copy(deep=True)
|
|
channels.send_tool_hints = True
|
|
config.channels = channels
|
|
assert config.channels.send_tool_hints is True
|
|
assert config.channels.feishu["appId"] == "test123"
|
|
|
|
|
|
class TestApiServerRegistration:
|
|
"""Tests for API Server menu registration."""
|
|
|
|
def test_api_server_in_settings_sections(self):
|
|
"""API Server should be registered in _SETTINGS_SECTIONS."""
|
|
from nanobot.cli.onboard import _SETTINGS_SECTIONS
|
|
|
|
assert "API Server" in _SETTINGS_SECTIONS
|
|
|
|
def test_api_server_getter_returns_api(self):
|
|
"""API Server getter should return config.api."""
|
|
from nanobot.cli.onboard import _SETTINGS_GETTER
|
|
|
|
config = Config()
|
|
result = _SETTINGS_GETTER["API Server"](config)
|
|
assert result is config.api
|
|
|
|
def test_api_server_setter_writes_api(self):
|
|
"""API Server setter should update config.api."""
|
|
from nanobot.cli.onboard import _SETTINGS_SETTER
|
|
|
|
config = Config()
|
|
from nanobot.config.schema import ApiConfig
|
|
|
|
new_api = ApiConfig(host="0.0.0.0", port=9999)
|
|
_SETTINGS_SETTER["API Server"](config, new_api)
|
|
assert config.api.host == "0.0.0.0"
|
|
assert config.api.port == 9999
|
|
|
|
|
|
class TestMainMenuUpdate:
|
|
"""Tests for main menu including new Channel Common and API Server items."""
|
|
|
|
def test_main_menu_hides_save_actions_until_needed(self):
|
|
"""The first screen should not show save or summary actions before edits."""
|
|
from nanobot.cli.onboard import _get_main_menu_choices
|
|
|
|
clean_choices = _get_main_menu_choices(False)
|
|
dirty_choices = _get_main_menu_choices(True)
|
|
|
|
assert clean_choices == [
|
|
"[Q] Quick Start (provider + key)",
|
|
"[A] Advanced Settings",
|
|
"[X] Exit",
|
|
]
|
|
assert "[S] Save and Exit" not in clean_choices
|
|
assert "[V] View Configuration Summary" not in clean_choices
|
|
assert "[S] Save and Exit" in dirty_choices
|
|
assert "[X] Exit Without Saving" in dirty_choices
|
|
|
|
def test_run_onboard_quick_start_edit(self, monkeypatch):
|
|
"""run_onboard should route [Q] to Quick Start."""
|
|
initial_config = Config()
|
|
|
|
responses = iter([
|
|
"[Q] Quick Start (provider + key)",
|
|
])
|
|
|
|
class FakePrompt:
|
|
def __init__(self, response):
|
|
self.response = response
|
|
|
|
def ask(self):
|
|
return self.response
|
|
|
|
def fake_select(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
def fake_quick_start(config):
|
|
config.agents.defaults.bot_name = "quickbot"
|
|
return True
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_main_menu_header", lambda: None)
|
|
monkeypatch.setattr(onboard_wizard, "questionary", SimpleNamespace(select=fake_select))
|
|
monkeypatch.setattr(onboard_wizard, "_configure_quick_start", fake_quick_start)
|
|
|
|
result = run_onboard(initial_config=initial_config)
|
|
|
|
assert result.should_save is True
|
|
assert result.config.agents.defaults.bot_name == "quickbot"
|
|
|
|
def test_quick_start_provider_choice_skips_advanced_prompts(self, monkeypatch):
|
|
"""The beginner path should ask for provider and API key without advanced settings."""
|
|
config = Config()
|
|
|
|
def fail_model_input(*_args, **_kwargs):
|
|
raise AssertionError("Quick Start should not ask for a model ID when /models works")
|
|
|
|
def fail_websocket_config(*_args, **_kwargs):
|
|
raise AssertionError("Quick Start should not open WebSocket settings")
|
|
|
|
pause_messages: list[str] = []
|
|
|
|
monkeypatch.setattr(onboard_wizard.console, "clear", lambda: None)
|
|
monkeypatch.setattr(onboard_wizard, "_show_section_header", lambda *a, **kw: None)
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", lambda *a, **kw: "DeepSeek")
|
|
monkeypatch.setattr(onboard_wizard, "_input_text", lambda *a, **kw: "sk-ds-test")
|
|
monkeypatch.setattr(onboard_wizard, "_fetch_first_quick_start_model", lambda *a, **kw: "deepseek-v4-flash")
|
|
monkeypatch.setattr(onboard_wizard, "_input_model_with_autocomplete", fail_model_input)
|
|
monkeypatch.setattr(onboard_wizard, "_configure_pydantic_model", fail_websocket_config)
|
|
monkeypatch.setattr(onboard_wizard, "_print_summary_panel", lambda *a, **kw: None)
|
|
monkeypatch.setattr(onboard_wizard, "_pause", lambda message="": pause_messages.append(message))
|
|
|
|
assert onboard_wizard._configure_quick_start(config) is True
|
|
|
|
assert pause_messages == ["Press Enter to save and exit..."]
|
|
assert config.providers.deepseek.api_key == "sk-ds-test"
|
|
assert config.providers.deepseek.api_base == "https://api.deepseek.com"
|
|
assert config.agents.defaults.model_preset == "primary"
|
|
assert config.model_presets["primary"].provider == "deepseek"
|
|
assert config.model_presets["primary"].model == "deepseek-v4-flash"
|
|
websocket = getattr(config.channels, "websocket")
|
|
assert websocket["enabled"] is True
|
|
assert websocket["websocketRequiresToken"] is True
|
|
|
|
def test_quick_start_provider_choice_fetches_models_from_selected_provider(self, monkeypatch):
|
|
"""Known providers should fetch models only from the selected provider base URL."""
|
|
config = Config()
|
|
calls: dict[str, str] = {}
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_quick_start_progress", lambda *_args: None)
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", lambda *a, **kw: "OpenRouter")
|
|
monkeypatch.setattr(onboard_wizard, "_input_text", lambda *a, **kw: "sk-or-test")
|
|
|
|
def fake_fetch(api_base, api_key):
|
|
calls["api_base"] = api_base
|
|
calls["api_key"] = api_key
|
|
return "openai/gpt-4o-mini"
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_fetch_first_quick_start_model", fake_fetch)
|
|
|
|
assert onboard_wizard._configure_quick_start_provider(config) is True
|
|
|
|
assert calls == {
|
|
"api_base": "https://openrouter.ai/api/v1",
|
|
"api_key": "sk-or-test",
|
|
}
|
|
assert config.providers.openrouter.api_key == "sk-or-test"
|
|
assert config.providers.openrouter.api_base == "https://openrouter.ai/api/v1"
|
|
assert config.model_presets["primary"].provider == "openrouter"
|
|
assert config.model_presets["primary"].model == "openai/gpt-4o-mini"
|
|
|
|
def test_quick_start_openai_fetches_models_without_storing_base(self, monkeypatch):
|
|
"""OpenAI should still support key-only setup via its SDK default endpoint."""
|
|
config = Config()
|
|
calls: dict[str, str] = {}
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_quick_start_progress", lambda *_args: None)
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", lambda *a, **kw: "OpenAI")
|
|
monkeypatch.setattr(onboard_wizard, "_input_text", lambda *a, **kw: "sk-openai-test")
|
|
|
|
def fake_fetch(api_base, api_key):
|
|
calls["api_base"] = api_base
|
|
calls["api_key"] = api_key
|
|
return "gpt-4o-mini"
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_fetch_first_quick_start_model", fake_fetch)
|
|
|
|
assert onboard_wizard._configure_quick_start_provider(config) is True
|
|
|
|
assert calls == {
|
|
"api_base": "https://api.openai.com/v1",
|
|
"api_key": "sk-openai-test",
|
|
}
|
|
assert config.providers.openai.api_key == "sk-openai-test"
|
|
assert config.providers.openai.api_base is None
|
|
assert config.model_presets["primary"].provider == "openai"
|
|
assert config.model_presets["primary"].model == "gpt-4o-mini"
|
|
|
|
def test_quick_start_custom_base_url_fetches_first_model(self, monkeypatch):
|
|
"""Unknown providers should use only the user-provided base URL to fetch models."""
|
|
config = Config()
|
|
text_answers = iter(["sk-custom-test", "https://api.example.test/v1"])
|
|
calls: dict[str, str] = {}
|
|
|
|
def fake_fetch(api_base, api_key):
|
|
calls["api_base"] = api_base
|
|
calls["api_key"] = api_key
|
|
return "custom-model"
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_quick_start_progress", lambda *_args: None)
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_select_with_back",
|
|
lambda *a, **kw: onboard_wizard._QUICK_START_CUSTOM_PROVIDER_CHOICE,
|
|
)
|
|
monkeypatch.setattr(onboard_wizard, "_input_text", lambda *a, **kw: next(text_answers))
|
|
monkeypatch.setattr(onboard_wizard, "_fetch_first_quick_start_model", fake_fetch)
|
|
|
|
assert onboard_wizard._configure_quick_start_provider(config) is True
|
|
|
|
assert calls == {"api_base": "https://api.example.test/v1", "api_key": "sk-custom-test"}
|
|
assert config.providers.custom.api_key == "sk-custom-test"
|
|
assert config.providers.custom.api_base == "https://api.example.test/v1"
|
|
assert config.model_presets["primary"].provider == "custom"
|
|
assert config.model_presets["primary"].model == "custom-model"
|
|
|
|
def test_quick_start_requires_api_key_before_setting_defaults(self, monkeypatch):
|
|
"""Quick Start should not create a ready-looking config without an API key."""
|
|
config = Config()
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_quick_start_progress", lambda *_args: None)
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", lambda *a, **kw: "DeepSeek")
|
|
monkeypatch.setattr(onboard_wizard, "_input_text", lambda *a, **kw: "")
|
|
|
|
assert onboard_wizard._configure_quick_start_provider(config) is False
|
|
|
|
assert config.providers.deepseek.api_key is None
|
|
assert config.providers.deepseek.api_base is None
|
|
assert config.providers.custom.api_key is None
|
|
assert config.providers.custom.api_base is None
|
|
assert "primary" not in config.model_presets
|
|
|
|
def test_quick_start_summary_calls_out_missing_api_key(self, monkeypatch):
|
|
"""Quick Start summary should not tell users to run gateway before adding a key."""
|
|
config = Config()
|
|
config.model_presets["primary"] = ModelPresetConfig(
|
|
model="deepseek-v4-flash",
|
|
provider="deepseek",
|
|
)
|
|
|
|
captured: dict[str, list[tuple[str, str]]] = {}
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_quick_start_progress", lambda *_args: None)
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_print_summary_panel",
|
|
lambda rows, _title: captured.setdefault("rows", rows),
|
|
)
|
|
|
|
onboard_wizard._show_quick_start_summary(config)
|
|
|
|
labels = [label for label, _value in captured["rows"]]
|
|
rows = dict(captured["rows"])
|
|
assert rows["Status"] == "DeepSeek API key missing"
|
|
assert "API key" in rows["Next"]
|
|
assert "nanobot gateway" in rows["Next"]
|
|
assert labels.index("Next") < labels.index("Open")
|
|
assert "Model" not in rows
|
|
assert "Entry point" not in rows
|
|
assert "API key" not in rows
|
|
assert "Defaults" not in rows
|
|
|
|
def test_configure_login_channel_defaults_to_login(self, monkeypatch):
|
|
"""The channel wizard should start login before exposing advanced fields."""
|
|
from nanobot.channels.base import BaseChannel
|
|
|
|
config = Config()
|
|
calls: dict[str, Any] = {}
|
|
|
|
class LoginConfig(BaseModel):
|
|
enabled: bool = False
|
|
|
|
class LoginChannel(BaseChannel):
|
|
name = "loginchat"
|
|
display_name = "Login Chat"
|
|
|
|
async def login(self, force: bool = False) -> bool:
|
|
calls["force"] = force
|
|
return True
|
|
|
|
async def start(self) -> None:
|
|
pass
|
|
|
|
async def stop(self) -> None:
|
|
pass
|
|
|
|
async def send(self, msg) -> None:
|
|
pass
|
|
|
|
def fail_configure(*_args, **_kwargs):
|
|
raise AssertionError("Default action should run login, not open advanced fields")
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_get_channel_names", lambda: {"loginchat": "Login Chat"})
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_get_channel_config_class",
|
|
lambda channel: LoginConfig if channel == "loginchat" else None,
|
|
)
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_get_channel_class",
|
|
lambda channel: LoginChannel if channel == "loginchat" else None,
|
|
)
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_select_with_back",
|
|
lambda *_args, **_kwargs: onboard_wizard._CHANNEL_LOGIN_CHOICE,
|
|
)
|
|
monkeypatch.setattr(onboard_wizard, "_configure_pydantic_model", fail_configure)
|
|
|
|
onboard_wizard._configure_channel(config, "loginchat")
|
|
|
|
loginchat = getattr(config.channels, "loginchat")
|
|
assert loginchat["enabled"] is True
|
|
assert calls == {"force": False}
|
|
|
|
def test_main_menu_dispatch_includes_channel_common(self):
|
|
"""Advanced menu dispatch should route [H] to Channel Common."""
|
|
|
|
# We verify by checking the dispatch table is set up correctly
|
|
# The menu items are defined inline in run_onboard, so we test
|
|
# that _configure_general_settings handles the new sections.
|
|
from nanobot.cli.onboard import _SETTINGS_GETTER, _SETTINGS_SECTIONS, _SETTINGS_SETTER
|
|
|
|
assert "Channel Common" in _SETTINGS_SECTIONS
|
|
assert "Channel Common" in _SETTINGS_GETTER
|
|
assert "Channel Common" in _SETTINGS_SETTER
|
|
|
|
def test_main_menu_dispatch_includes_api_server(self):
|
|
"""Advanced menu dispatch should route [I] to API Server."""
|
|
from nanobot.cli.onboard import _SETTINGS_GETTER, _SETTINGS_SECTIONS, _SETTINGS_SETTER
|
|
|
|
assert "API Server" in _SETTINGS_SECTIONS
|
|
assert "API Server" in _SETTINGS_GETTER
|
|
assert "API Server" in _SETTINGS_SETTER
|
|
|
|
def test_run_onboard_channel_common_edit(self, monkeypatch):
|
|
"""run_onboard should handle [H] Channel Common through Advanced Settings."""
|
|
initial_config = Config()
|
|
|
|
responses = iter([
|
|
"[A] Advanced Settings",
|
|
"[H] Channel Common",
|
|
KeyboardInterrupt(),
|
|
"[S] Save and Exit",
|
|
])
|
|
|
|
class FakePrompt:
|
|
def __init__(self, response):
|
|
self.response = response
|
|
|
|
def ask(self):
|
|
if isinstance(self.response, BaseException):
|
|
raise self.response
|
|
return self.response
|
|
|
|
def fake_select(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
def fake_configure_general_settings(config, section):
|
|
if section == "Channel Common":
|
|
config.channels.send_tool_hints = True
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_main_menu_header", lambda: None)
|
|
monkeypatch.setattr(onboard_wizard, "questionary", SimpleNamespace(select=fake_select))
|
|
monkeypatch.setattr(onboard_wizard, "_configure_general_settings", fake_configure_general_settings)
|
|
|
|
result = run_onboard(initial_config=initial_config)
|
|
|
|
assert result.should_save is True
|
|
assert result.config.channels.send_tool_hints is True
|
|
|
|
def test_run_onboard_api_server_edit(self, monkeypatch):
|
|
"""run_onboard should handle [I] API Server through Advanced Settings."""
|
|
initial_config = Config()
|
|
|
|
responses = iter([
|
|
"[A] Advanced Settings",
|
|
"[I] API Server",
|
|
KeyboardInterrupt(),
|
|
"[S] Save and Exit",
|
|
])
|
|
|
|
class FakePrompt:
|
|
def __init__(self, response):
|
|
self.response = response
|
|
|
|
def ask(self):
|
|
if isinstance(self.response, BaseException):
|
|
raise self.response
|
|
return self.response
|
|
|
|
def fake_select(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
def fake_configure_general_settings(config, section):
|
|
if section == "API Server":
|
|
config.api.port = 9999
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_main_menu_header", lambda: None)
|
|
monkeypatch.setattr(onboard_wizard, "questionary", SimpleNamespace(select=fake_select))
|
|
monkeypatch.setattr(onboard_wizard, "_configure_general_settings", fake_configure_general_settings)
|
|
|
|
result = run_onboard(initial_config=initial_config)
|
|
|
|
assert result.should_save is True
|
|
assert result.config.api.port == 9999
|
|
|
|
def test_view_summary_calls_pause(self, monkeypatch):
|
|
"""Advanced [V] View Summary should pause before returning to the menu."""
|
|
initial_config = Config()
|
|
pause_called = {"n": 0}
|
|
|
|
responses = iter([
|
|
"[A] Advanced Settings",
|
|
"[V] View Configuration Summary",
|
|
KeyboardInterrupt(),
|
|
"[X] Exit",
|
|
])
|
|
|
|
class FakePrompt:
|
|
def __init__(self, response):
|
|
self.response = response
|
|
|
|
def ask(self):
|
|
if isinstance(self.response, BaseException):
|
|
raise self.response
|
|
return self.response
|
|
|
|
def fake_select(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
def fake_pause():
|
|
pause_called["n"] += 1
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_show_main_menu_header", lambda: None)
|
|
monkeypatch.setattr(onboard_wizard, "questionary", SimpleNamespace(select=fake_select))
|
|
# _pause is called inside _show_summary, so we patch it there
|
|
monkeypatch.setattr(onboard_wizard, "_pause", fake_pause)
|
|
# Suppress summary output but still call _pause
|
|
monkeypatch.setattr(onboard_wizard, "_print_summary_panel", lambda *a, **kw: None)
|
|
monkeypatch.setattr(onboard_wizard, "_get_provider_names", lambda: {})
|
|
monkeypatch.setattr(onboard_wizard, "_get_channel_names", lambda: {})
|
|
|
|
result = run_onboard(initial_config=initial_config)
|
|
|
|
assert result.should_save is False
|
|
assert pause_called["n"] == 1
|
|
|
|
|
|
class TestInputTextEmptyString:
|
|
"""Tests for _input_text empty-string handling bug fix."""
|
|
|
|
def test_empty_string_returned_not_none(self, monkeypatch):
|
|
"""_input_text should return empty string, not None, when user enters ''."""
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_get_questionary",
|
|
lambda: SimpleNamespace(text=lambda *a, **kw: SimpleNamespace(ask=lambda: "")),
|
|
)
|
|
|
|
result = _input_text("Name", "old", "str")
|
|
assert result == ""
|
|
|
|
def test_none_still_returns_none(self, monkeypatch):
|
|
"""_input_text should return None when questionary returns None."""
|
|
monkeypatch.setattr(
|
|
onboard_wizard,
|
|
"_get_questionary",
|
|
lambda: SimpleNamespace(text=lambda *a, **kw: SimpleNamespace(ask=lambda: None)),
|
|
)
|
|
|
|
result = _input_text("Name", "old", "str")
|
|
assert result is None
|
|
|
|
|
|
class TestIsStrOrNone:
|
|
"""Tests for _is_str_or_none helper."""
|
|
|
|
def test_str_or_none_true(self):
|
|
from nanobot.cli.onboard import _is_str_or_none
|
|
|
|
assert _is_str_or_none(str | None) is True
|
|
|
|
def test_optional_str_true(self):
|
|
from typing import Optional
|
|
|
|
from nanobot.cli.onboard import _is_str_or_none
|
|
|
|
assert _is_str_or_none(Optional[str]) is True
|
|
|
|
def test_str_only_false(self):
|
|
from nanobot.cli.onboard import _is_str_or_none
|
|
|
|
assert _is_str_or_none(str) is False
|
|
|
|
def test_int_or_none_false(self):
|
|
from nanobot.cli.onboard import _is_str_or_none
|
|
|
|
assert _is_str_or_none(int | None) is False
|
|
|
|
|
|
class TestConfigurePydanticModelEmptyString:
|
|
"""Tests that optional string fields are cleared when empty string is entered."""
|
|
|
|
def test_optional_str_empty_string_becomes_none(self, monkeypatch):
|
|
"""Entering '' for an optional str field should set it to None."""
|
|
from pydantic import BaseModel
|
|
|
|
|
|
class M(BaseModel):
|
|
api_key: str | None = None
|
|
|
|
model = M(api_key="secret")
|
|
|
|
call_count = {"select": 0}
|
|
|
|
def fake_select(_prompt, choices, default=None):
|
|
call_count["select"] += 1
|
|
# First call: select the api_key field, then Done
|
|
if call_count["select"] == 1:
|
|
for c in choices:
|
|
if "Api Key" in c:
|
|
return c
|
|
return choices[0]
|
|
return "[Done]"
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", fake_select)
|
|
monkeypatch.setattr(onboard_wizard, "_show_config_panel", lambda *a, **kw: None)
|
|
# Simulate user entering empty string
|
|
monkeypatch.setattr(
|
|
onboard_wizard, "_input_with_existing", lambda *a, **kw: ""
|
|
)
|
|
|
|
result = _configure_pydantic_model(model, "Test")
|
|
assert result is not None
|
|
assert result.api_key is None
|
|
|
|
def test_required_str_empty_string_kept(self, monkeypatch):
|
|
"""Entering '' for a required str field should keep the empty string."""
|
|
from pydantic import BaseModel
|
|
|
|
class M(BaseModel):
|
|
api_key: str = ""
|
|
|
|
model = M(api_key="secret")
|
|
|
|
call_count = {"select": 0}
|
|
|
|
def fake_select(_prompt, choices, default=None):
|
|
call_count["select"] += 1
|
|
if call_count["select"] == 1:
|
|
for c in choices:
|
|
if "Api Key" in c:
|
|
return c
|
|
return choices[0]
|
|
return "[Done]"
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", fake_select)
|
|
monkeypatch.setattr(onboard_wizard, "_show_config_panel", lambda *a, **kw: None)
|
|
monkeypatch.setattr(
|
|
onboard_wizard, "_input_with_existing", lambda *a, **kw: ""
|
|
)
|
|
|
|
result = _configure_pydantic_model(model, "Test")
|
|
assert result is not None
|
|
assert result.api_key == ""
|
|
|
|
|
|
class TestModelPresetWizard:
|
|
"""Tests for model preset CRUD in the onboard wizard."""
|
|
|
|
def test_sync_preset_cache(self):
|
|
"""_sync_preset_cache should populate the module-level cache."""
|
|
from nanobot.cli.onboard import _MODEL_PRESET_CACHE, _sync_preset_cache
|
|
from nanobot.config.schema import ModelPresetConfig
|
|
|
|
config = Config()
|
|
config.model_presets["fast"] = ModelPresetConfig(model="gpt-4.1-mini")
|
|
config.model_presets["power"] = ModelPresetConfig(model="gpt-4.1")
|
|
_sync_preset_cache(config)
|
|
assert _MODEL_PRESET_CACHE == {"fast", "power"}
|
|
_MODEL_PRESET_CACHE.clear()
|
|
|
|
def test_model_preset_add(self, monkeypatch):
|
|
"""_configure_model_presets should add a new preset."""
|
|
from nanobot.cli.onboard import _MODEL_PRESET_CACHE, _configure_model_presets
|
|
from nanobot.config.schema import ModelPresetConfig
|
|
|
|
config = Config()
|
|
_MODEL_PRESET_CACHE.clear()
|
|
|
|
responses = iter([
|
|
"[+] Add new preset",
|
|
"my-preset",
|
|
"<- Back",
|
|
])
|
|
|
|
class FakePrompt:
|
|
def __init__(self, response):
|
|
self.response = response
|
|
|
|
def ask(self):
|
|
if isinstance(self.response, BaseException):
|
|
raise self.response
|
|
return self.response
|
|
|
|
def fake_select(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
def fake_text(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
def fake_configure(*_model, **_kwargs):
|
|
return ModelPresetConfig(model="gpt-test", temperature=0.5)
|
|
|
|
def fake_select_with_back(*_args, **_kwargs):
|
|
return next(responses)
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", fake_select_with_back)
|
|
monkeypatch.setattr(
|
|
onboard_wizard, "questionary", SimpleNamespace(select=fake_select, text=fake_text)
|
|
)
|
|
monkeypatch.setattr(onboard_wizard, "_configure_pydantic_model", fake_configure)
|
|
monkeypatch.setattr(onboard_wizard, "_show_section_header", lambda *a, **kw: None)
|
|
monkeypatch.setattr(onboard_wizard, "console", SimpleNamespace(clear=lambda: None))
|
|
|
|
_configure_model_presets(config)
|
|
|
|
assert "my-preset" in config.model_presets
|
|
assert config.model_presets["my-preset"].model == "gpt-test"
|
|
assert config.model_presets["my-preset"].temperature == 0.5
|
|
_MODEL_PRESET_CACHE.clear()
|
|
|
|
def test_model_preset_delete(self, monkeypatch):
|
|
"""_configure_model_presets should delete an existing preset."""
|
|
from nanobot.cli.onboard import _MODEL_PRESET_CACHE, _configure_model_presets
|
|
from nanobot.config.schema import ModelPresetConfig
|
|
|
|
config = Config()
|
|
config.model_presets["old"] = ModelPresetConfig(model="x")
|
|
_MODEL_PRESET_CACHE.clear()
|
|
_MODEL_PRESET_CACHE.update({"old", "default"})
|
|
|
|
responses = iter([
|
|
"old (x)",
|
|
"Delete",
|
|
True,
|
|
"<- Back",
|
|
])
|
|
|
|
class FakePrompt:
|
|
def __init__(self, response):
|
|
self.response = response
|
|
|
|
def ask(self):
|
|
if isinstance(self.response, BaseException):
|
|
raise self.response
|
|
return self.response
|
|
|
|
def fake_select(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
def fake_confirm(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
def fake_select_with_back(*_args, **_kwargs):
|
|
return next(responses)
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", fake_select_with_back)
|
|
monkeypatch.setattr(
|
|
onboard_wizard, "questionary", SimpleNamespace(select=fake_select, confirm=fake_confirm)
|
|
)
|
|
monkeypatch.setattr(onboard_wizard, "_show_section_header", lambda *a, **kw: None)
|
|
monkeypatch.setattr(onboard_wizard, "console", SimpleNamespace(clear=lambda: None))
|
|
|
|
_configure_model_presets(config)
|
|
|
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assert "old" not in config.model_presets
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assert "old" not in _MODEL_PRESET_CACHE
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_MODEL_PRESET_CACHE.clear()
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|
|
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def test_model_preset_field_handler(self, monkeypatch):
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"""_handle_model_preset_field should set a preset name from choices."""
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from nanobot.cli.onboard import _MODEL_PRESET_CACHE, _handle_model_preset_field
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from nanobot.config.schema import AgentDefaults
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|
|
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_MODEL_PRESET_CACHE.clear()
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_MODEL_PRESET_CACHE.update({"fast", "power", "default"})
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|
|
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monkeypatch.setattr(onboard_wizard, "_select_with_back", lambda *a, **kw: "fast")
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|
|
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defaults = AgentDefaults()
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_handle_model_preset_field(defaults, "model_preset", "Model Preset", None)
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assert defaults.model_preset == "fast"
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_MODEL_PRESET_CACHE.clear()
|
|
|
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def test_model_preset_field_handler_clear(self, monkeypatch):
|
|
"""_handle_model_preset_field should clear preset when (clear/unset) chosen."""
|
|
from nanobot.cli.onboard import _MODEL_PRESET_CACHE, _handle_model_preset_field
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from nanobot.config.schema import AgentDefaults
|
|
|
|
_MODEL_PRESET_CACHE.clear()
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_MODEL_PRESET_CACHE.add("fast")
|
|
|
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monkeypatch.setattr(onboard_wizard, "_select_with_back", lambda *a, **kw: "(clear/unset)")
|
|
|
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defaults = AgentDefaults(model_preset="fast")
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_handle_model_preset_field(defaults, "model_preset", "Model Preset", "fast")
|
|
assert defaults.model_preset is None
|
|
_MODEL_PRESET_CACHE.clear()
|
|
|
|
def test_main_menu_dispatch_includes_model_presets(self):
|
|
"""_configure_model_presets should be importable and callable."""
|
|
from nanobot.cli.onboard import _configure_model_presets
|
|
|
|
assert callable(_configure_model_presets)
|
|
|
|
def test_run_onboard_model_presets_edit(self, monkeypatch):
|
|
"""run_onboard should handle [M] Model Presets through Advanced Settings."""
|
|
from nanobot.config.schema import ModelPresetConfig
|
|
|
|
initial_config = Config()
|
|
|
|
responses = iter([
|
|
"[A] Advanced Settings",
|
|
"[M] Model Presets",
|
|
KeyboardInterrupt(),
|
|
"[S] Save and Exit",
|
|
])
|
|
|
|
class FakePrompt:
|
|
def __init__(self, response):
|
|
self.response = response
|
|
|
|
def ask(self):
|
|
if isinstance(self.response, BaseException):
|
|
raise self.response
|
|
return self.response
|
|
|
|
def fake_select(*_args, **_kwargs):
|
|
return FakePrompt(next(responses))
|
|
|
|
preset_mutated = {"n": 0}
|
|
|
|
def fake_configure_model_presets(config):
|
|
preset_mutated["n"] += 1
|
|
config.model_presets["test"] = ModelPresetConfig(model="gpt-test")
|
|
|
|
monkeypatch.setattr(onboard_wizard, "questionary", SimpleNamespace(select=fake_select))
|
|
monkeypatch.setattr(onboard_wizard, "_configure_model_presets", fake_configure_model_presets)
|
|
monkeypatch.setattr(onboard_wizard, "_show_main_menu_header", lambda: None)
|
|
monkeypatch.setattr(onboard_wizard, "_show_section_header", lambda *a, **kw: None)
|
|
monkeypatch.setattr(onboard_wizard, "console", SimpleNamespace(clear=lambda: None))
|
|
|
|
result = run_onboard(initial_config)
|
|
assert result.should_save is True
|
|
assert preset_mutated["n"] == 1
|
|
assert "test" in result.config.model_presets
|
|
|
|
def test_fallback_models_field_add(self, monkeypatch):
|
|
"""_handle_fallback_models_field should add a preset name."""
|
|
from nanobot.cli.onboard import _MODEL_PRESET_CACHE, _handle_fallback_models_field
|
|
from nanobot.config.schema import AgentDefaults
|
|
|
|
_MODEL_PRESET_CACHE.clear()
|
|
_MODEL_PRESET_CACHE.update({"fast", "default"})
|
|
|
|
select_responses = iter(["fast"])
|
|
questionary_responses = iter(["[+] Add preset", "[Done]"])
|
|
|
|
class FakePrompt:
|
|
def __init__(self, response):
|
|
self.response = response
|
|
|
|
def ask(self):
|
|
if isinstance(self.response, BaseException):
|
|
raise self.response
|
|
return self.response
|
|
|
|
def fake_questionary_select(*_args, **_kwargs):
|
|
return FakePrompt(next(questionary_responses))
|
|
|
|
def fake_select_with_back(*_args, **_kwargs):
|
|
return next(select_responses)
|
|
|
|
monkeypatch.setattr(
|
|
onboard_wizard, "questionary",
|
|
SimpleNamespace(select=fake_questionary_select, press_any_key_to_continue=lambda: FakePrompt(None)),
|
|
)
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", fake_select_with_back)
|
|
monkeypatch.setattr(onboard_wizard, "console", SimpleNamespace(clear=lambda: None, print=lambda *a, **kw: None))
|
|
|
|
defaults = AgentDefaults()
|
|
_handle_fallback_models_field(defaults, "fallback_models", "Fallback Models", [])
|
|
assert defaults.fallback_models == ["fast"]
|
|
_MODEL_PRESET_CACHE.clear()
|
|
|
|
def test_provider_field_handler(self, monkeypatch):
|
|
"""_handle_provider_field should set provider from choices."""
|
|
from nanobot.cli.onboard import _handle_provider_field
|
|
from nanobot.config.schema import AgentDefaults
|
|
|
|
monkeypatch.setattr(onboard_wizard, "_select_with_back", lambda *a, **kw: "anthropic")
|
|
|
|
defaults = AgentDefaults()
|
|
_handle_provider_field(defaults, "provider", "Provider", "auto")
|
|
assert defaults.provider == "anthropic"
|